Metrology method and system

Abstract

A control system for use in metrology measurements of patterned structures is presented, which comprises a data processing system comprising: a dimensional metrology geometrical engine providing a geometric model of a predetermined region of interest (RO I) of the structure; an image processing recipe provider generating data indicative of predefined image processing recipe; a synthetic image data generator utilizing said geometrical model and generating image data corresponding to a synthetic HRI image data of said ROI; and an optimization module configured for: processing input correlation data between first and second HRI-related data being, respectively, non-image data indicative of predefined position information associated with measured geometric parameter(s) of the ROI and data indicative of said synthetic HRI image data; and generating an optimized geometric model of the ROI for use in the metrology measurements of the structure, thereby eliminating a need for measured HRI image data of the ROI for model optimization.

Description

[0001] METROLOGY METHOD AND SYSTEM

[0002] TECHNOLOGICAL FIELD AND BACKGROUND

[0003] The present disclosure is generally in the field of metrology techniques for measuring various parameters of patterned structures, such as semiconductor wafers, and can be used for example for controlling one or more processes in the manufacture of such structures.

[0004] Protecting confidential information in Integrated Circuit (IC) manufacturing from owners or vendors of dimensional metrology is crucial to safeguard proprietary designs, processes, and intellectual property (IP). The metrology vendor requires access to geometrical parameters (such as critical dimensions, layer thickness, and line edge roughness) to perform precise measurements, but excessive disclosure may expose proprietary details of the IC design and fabrication process.

[0005] Several techniques are used to achieve confidentiality between an IC manufacturer and a dimensional metrology owner / vendor, among them homomorphic encryption which allows computations on encrypted data, preventing vendors from accessing raw design information, restricting access to metrology data based on roles and permissions, or process metrology data locally on secure chips instead of sending it to external systems.

[0006] GENERAL DESCRIPTION

[0007] There is a need in the art for novel techniques for accurate metrology (dimensional) measurements on a structure utilizing model-based measured data interpretation, based on data relayed to / extracted from high-resolution image (HRI) data, while avoiding a need for HRI data generator (i.e., "owner" of HRI data) to disclose sensitive HRI information which might be indicative of the structure's features related to the confidential aspects of the manufacturing process of the structure (e.g., covered by intellectual property rights of the structure's manufacturer). High resolution imaging (HRI) techniques, such as Transmission Electron Microscopy (TEM, Scanning Transmission Electron Microscopy (STEM), Energy Dispersive X-ray Spectroscopy (EDX), Electron Energy Loss Spectroscopy (EELS), provide detailed insights into structure's features. If a metrology vendor has unrestricted access to such data, said metrology vendor (or third party) could perform reverse engineering of the manufacturing process.

[0008] For example, next-generation semiconductor processes involve unique gate-all- around (GAA) or FinFET structures. If metrology vendors gain unrestricted access to critical dimensions (CD), fin heights, or gate lengths, competitors could copy or optimize similar processes. Memory manufacturers use highly complex multi-layer stacking techniques. If precise geometrical details and possibly also material-related details (e.g., layer thickness, pitch, aspect ratios, order of layers, outline of shapes, presence of absence of some materials) leak, it could allow competitors to improve yield and performance based on another company’s research. Many integrated circuits (ICs) incorporate security-critical structures like Physical Unclonable Functions (PUFs) or hidden cryptographic elements. Exposure of detailed geometrical parameters to third parties could compromise hardware security, enabling attacks like side-channel analysis.

[0009] The present disclosure provides a novel approach, according to which, an "owner" of the high resolution image data of a structure (like integrated circuit manufacturer or tool vendor) can provide to a metrology measurement system (or metrology data interpretation system) a few predefined geometric parameters, being HRLrelated nonimage data and being extracted from the HRI data of the structure (instead of providing raw HRI data), while this HRLrelated non-image data can be effectively used by the technique of the present invention to perform model-based metrology measurements on the structure.

[0010] In the description below, the high-resolution imaging technique is described in relation to TEM technique. However, it should be noted that the principles of the technique of the present disclosure are not limited to this specific type of high-resolution imaging, and therefore the terms "TEM image data", "TEM-related non-image data" or "TEM-related non-image measured data", "simulated TEM image data" should be interpreted broadly covering also respective aspects of other types of high-resolution imaging such as STEM, EDX, EELS, as well as different acquisition modes thereof, such as bright-field, dark-field, high angle annular dark field and other modes, e.g., backscattering.

[0011] The present disclosure provides a system and method allowing an owner of raw measured TEM images (e.g., the TEM tool vendor, an Integrated Circuit (IC) manufacturer) to automatically carry out certain operations on the raw TEM images and extract therefrom and provide (automatically or manually) to a dimensional metrology owner or vendor only predefined position information associated with one or more geometrical parameters without disclosing the image itself, thus protecting the sensitive / confidential information / data associated with the image (intellectual property of the IC manufactured). The present disclosure provides the novel technique which can be used to enable effective metrology measurements on a structure without a need for sensitive / confidential (IP-protected) high-resolution images produced by a TEM tool (e.g., TEM, EDX or EELS images).

[0012] More specifically, the present disclosure enhances / optimizes the metrology measurements, in particular model -based metrology measurements utilizing TEM data. Such metrology measurements utilizing TEM data are described for example in US 10,916,404 US 11450541 (hereby incorporated by reference in their entirety), both assigned to the assignee of the present application.

[0013] Specifically, the present disclosure introduces the concept of “Data with positions” which allows specific feature measurements to be located spatially without revealing the entire high-resolution image. Additionally, the present disclosure provides a novel technique for comparing extracted parameters (HRLrelated non-image data) with simulated high-resolution images (of the similar high-resolution imaging technique) to improve / optimize dimensional metrology models. In some embodiments, different approaches are provided to automate the comparison and optimization processes while eliminating a need for sensitive (confidential) high-resolution image data.

[0014] In general, the shared geometric parameters may include structure widths at various height percentages (10%, 30%, 50%, 70%, 90%), heights of features of interest, parameters of interest (such as rounding, undercut, over-etch, etc.), and measurements across multiple devices in a TEM lamella. The positions of these measurements within the image (termed here as “Data with Positions”) can be included (with x,y coordinates) without revealing the entire image. The extracted parameters are then compared with simulated TEM images based on dimensional metrology models (e.g., optical critical dimension models), and this comparison is used to enhance the accuracy of the dimensional metrology model.

[0015] It should be noted that dimensional metrology techniques typically include various spectroscopy-based techniques (e.g., spectral-based reflectometry) to measure the 2D / 3D parameters of the patterned structures. These techniques provide an indirect (e.g., spectral) response from the patterned structure which is a function of the various structural parameters of the structure (e.g., materials, dimensions, side wall angles, etc.). Interpretation of measured data obtained by these spectral is typically based on modeling. The modeling creates a 2D / 3D model of the patterned structure, utilizing available material and dimensional data. Based on this model, the electromagnetic response of the patterned structure is modeled, such that the response obtained from the patterned structure during the dimensional metrology measurements can be translated into the required 2D / 3D parameters (e.g., materials, dimensions, side wall angles, etc.) of the patterned structure.

[0016] TEM images of the patterned structure provide reference data about the geometry of patterned structure. The geometric data extracted from TEM images is correlated with the corresponding geometric data extracted from the (e.g., spectral) dimensional metrology measurements and directly affects the interpretation of the (e.g., spectral) dimensional metrology measurements. It should also be noted that the measured dimensional metrology data is typically compared with the corresponding simulated (e.g., spectral) data, since direct (“ground truth”) measurement of the critical geometrical parameters is not available. This is where geometric data extracted from the TEM images enters the interpretation process, by providing the missing “ground truth” data to allow, on one hand, a more accurate interpretation of the dimensional metrology data, and on the other hand, an optimization of the 2D / 3D model of the patterned structure, which in turn improves the models of the electromagnetic response.

[0017] Typically, the data provided by the owners of raw TEM images is limited to a single critical dimension (CD) parameter characterizing the geometrical parameter of interest. Frequently, this limited information is not enough to allow the dimensional metrology-based technique to reach accurate enough results. It is therefore important for the dimensional metrology owner or vendor to obtain as much critical geometric data extracted from the TEM images as possible, while protecting the sensitive / confidential information / data associated with the images.

[0018] Frequently, although the number of possible geometrical parameters may be very large (e.g., more than 30), the number of critical parameters (termed at times ‘parameters of interest’) defining the functionality of a specific patterned structure may be small (e.g., 3-4 parameters). As mentioned above, the owners of raw TEM images may provide dimensional metrology owner or vendor with only a single critical dimension (e.g., structure width at 50% height) related to the patterned structure, whereas additional geometrical parameters (e.g., structure width at 10% and 90% height) of the patterned structure may significantly increase the accuracy of the dimensional metrology measurements.

[0019] The present disclosure provides a technique that allows the owners of raw TEM images to provide sufficient geometrical data extracted from the TEM images without compromising the confidentiality of the overall information / data associated with the TEM images. For example, analysis of TEM images may provide some edge points of an element belonging to the patterned structure. The technique of the present disclosure allows the owner of raw TEM images to provide only the required edge points in a data format that guarantees protection of confidential information.

[0020] In the description below all types of images produced by a TEM tool (e.g. TEM, EDX or EELS images) are referred to as TEM images. Furthermore, an owner of raw TEM images (e.g., TEM, EDX, EELS images) is at times referred to as IP owner. A system / entity providing such TEM-related non-image data is termed herein as "TEM data provider".

[0021] It should be noted, and is exemplified further below, that the technique of the present disclosure in its entirety can be implemented by a control system associated with metrology measurement system, or the functional parts of such control system can be distributed between control systems of the metrology measurement side and TEM data provider side.

[0022] Thus, according to one broad aspect of the present disclosure, it provides a control system for use in metrology measurements of a patterned structure, the control system being configured and operable for data communication with a High Resolution Imaging (HRI) data provider and comprising a computerized system comprising data input and output utilities, memory, and a data processing system, wherein the data processing system comprises: a dimensional metrology geometrical engine configured and operable to provide a geometric model of a predetermined region of interest (ROI) of the patterned structure; an image processing recipe provider configured and operable to generate data indicative of predefined image processing recipe; a synthetic (simulated) image data generator configured and operable to utilize said geometrical model of the predetermined ROI and generate image data corresponding to a synthetic HRI image data of said predetermined ROI; an optimization module configured and operable to carry out the following: process input correlation data indicative of a level of fit between first HRI-related data being a non-image measured data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure, and second HRI-related data comprising data indicative of said synthetic HRI image data, and generate an optimized geometric model of said predetermined ROI, thereby allowing use of the optimized geometric model for the metrology measurements of the patterned structure, while eliminating a need for measured HRI image data of the ROI for optimization of the geometric model used in the metrology measurements.

[0023] The HRI image data may comprise any one of the following data providing detailed insights into features of the patterned structure: Transmission Electron Microscopy (TEM) data, Scanning Transmission Electron Microscopy (STEM) data, Energy Dispersive X-ray Spectroscopy (EDX) data, Electron Energy Loss Spectroscopy (EELS).

[0024] In some embodiments, the control system is configured and operable to carry out the following: transmit to the HRI data provider data indicative of the predefined image processing recipe thereby enabling the HRI data provider to generate said first non-image data using predefined image processing recipe. According to some examples, the control system is configured and operable to transmit data indicative of the synthetic HRI image data to the HRI data provider and receive said correlation data from the HRI data provider. According to some other examples, the control system is configured and operable to receive said first non-image HRI-related data from the HRI data provider, and further comprises an analyzer configured and operable to receive and analyze said first and second HRI-related data and generate the correlation data.

[0025] The second HRI-related data may comprise a synthetic HRI image.

[0026] In some embodiments, said second HRI-related data comprises simulated position information associated with said one or more geometric parameters of the ROI of the patterned structure, extracted from the synthetic HRI image data using said predefined image processing recipe.

[0027] In some embodiments, said correlation data comprises a matching score for overlay of each of said one or more measured geometric parameters on top of said synthetic HRI image.

[0028] In some embodiments, said correlation data comprises matching scores indicative of a match between the predefined position information and corresponding edges identified in the synthetic HRI image data.

[0029] In some embodiments, said correlation data comprises an automatically calculated merit function describing the level of fit between the first and second HRI-related data.

[0030] In some embodiments, the predefined position information associated with one or more measured geometric parameters of the ROI comprises X and Y coordinates in a measured HRI image plane relative to an image origin.

[0031] The predefined position information for at least one of said one or more geometric parameters may comprise: start and end points for a measured feature, and / or a single point, and / or three or more points.

[0032] In some embodiments, the one or more geometric parameters comprise one or more of the following: widths' values at corresponding parts of a total height of a feature, heights of features, parameters of interest including rounding, undercut, over-etch, underetch, gap distance, or diagonal distances.

[0033] In some embodiments, the optimization comprises adjustment of one or more parameters of the geometric model, based on the correlation data indicative of visual comparison of the predefined position information overlaid on the synthetic HRI image data. According to another broad aspect of the present disclosure, it provides a control system for use in metrology measurements of a patterned structure, the control system comprising a computerized system comprising data input and output utilities, memory, and a data processing system, wherein the data processing system comprises: a dimensional metrology geometrical engine configured and operable to provide a geometric model of a predetermined region of interest (ROI) of the patterned structure; a synthetic (simulated) image data generator configured and operable to utilize said geometrical model of the predetermined ROI and generate image data corresponding to a synthetic High Resolution Image (HRI) data of said predetermined ROI; an analyzer configured and operable to receive and analyze first and second HRI- related data and generate correlation data indicative of a level of fit between said first and second HRI-related data, wherein: the first HRI-related data is a non-image measured data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure, and the second TEM-related data comprises data indicative of said synthetic TEM image data,; an optimization module configured and operable to utilize said correlation data to perform optimization of the geometric model of said predetermined ROI and generate an optimized geometric model , thereby allowing use of the optimized geometric model for the metrology measurements of the patterned structure, while eliminating a need for measured HRI image data of the ROI for optimization of the geometric model used in the metrology measurements.

[0034] As indicated above, said HRI data may comprise any one of the following data providing detailed insights into features of the patterned structure: Transmission Electron Microscopy (TEM) data, Scanning Transmission Electron Microscopy (STEM) data, Energy Dispersive X-ray Spectroscopy (EDX) data, Electron Energy Loss Spectroscopy (EELS).

[0035] The first HRI-related data, being the non-image measured data, may be extracted from an HRI measured image of said ROI using a predefined image processing recipe. The second HRI-related data may comprise a synthetic HRI image; or may comprise simulated position information associated with said one or more geometric parameters of the ROI of the patterned structure, extracted from the synthetic HRI data using said predefined image processing recipe. The correlation data may comprise a matching score for overlay of each of said one or more measured geometric parameters on top of said synthetic HRI image; or may comprise matching scores indicative of a match between the predefined position information and corresponding edges identified in the synthetic HRI image data. For example, the correlation data comprises an automatically calculated merit function describing the level of fit between the first and second HRI-related data.

[0036] The predefined position information associated with one or more measured geometric parameters of the ROI comprises X and Y coordinates in a measured HRI image plane relative to an image origin.

[0037] Alternatively or additionally, the predefined position information for at least one of said one or more geometric parameters comprises start and end points for a measured feature; and / or a single point; and / or three or more points.

[0038] The one or more geometric parameters may comprise one or more of the following: widths' values at corresponding parts of a total height of a feature, heights of features, parameters of interest including rounding, undercut, over-etch, under-etch, gap distance, or diagonal distances.

[0039] The optimization may comprise adjustment of one or more parameters of the geometric model, based on the correlation data indicative of visual comparison of the predefined position information overlaid on the synthetic TEM image data.

[0040] The present disclosure also provides a method for use in metrology measurements of a patterned structure, the method comprising: providing a geometric model of a predetermined region of interest (ROI) of the patterned structure; providing data indicative of predefined image processing recipe; utilizing said geometrical model of the predetermined ROI and providing simulated image data corresponding to a synthetic High Resolution Imaging (HRI) image data of said predetermined ROI; processing correlation data indicative of a level of fit between first HRI-related data being a non-image measured data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure, and second HRI-related data comprising data indicative of said synthetic HRI image data, and generating an optimized geometric model of said predetermined ROI, thereby allowing use of the optimized geometric model for the metrology measurements of the patterned structure, while eliminating a need for measured HRI image data of the ROI for optimization of the geometric model used in the metrology measurements.

[0041] The method may comprise communicating with an HRI data provider to transmit to the HRI data provider data indicative of the predefined image processing recipe thereby enabling the HRI data provider to generate said first non-image data using predefined image processing recipe. For example, the method comprises transmitting data indicative of the synthetic HRI image data to the HRI data provider, and receiving said correlation data from the HRI data provider; or receiving said first non-image HRI-related data from the HRI data provider further and analyzing said first and second HRI-related data and generating the correlation data.

[0042] In yet further broad aspect of the present disclosure, it provides a method for use in metrology measurements of a patterned structure, the method comprising: providing a geometric model of a predetermined region of interest (ROI) of the patterned structure; providing data indicative of predefined image processing recipe; utilizing said geometrical model of the predetermined ROI and providing simulated image data corresponding to a synthetic High Resolution Imaging (HRI) image data of said predetermined ROI; analyzing first HRI-related data being a non-image measured data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure, and second HRI-related data comprising data indicative of said synthetic HRI image data, and generating correlation data indicative of a level of fit between the first and second HRI-related data; processing the correlation data and generating an optimized geometric model of said predetermined ROI, thereby allowing use of the optimized geometric model for the metrology measurements of the patterned structure, while eliminating a need for measured HRI image data of the ROI for optimization of the geometric model used in the metrology measurements. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to better understand the subject matter that is disclosed herein and to exemplify how it may be carried out in practice, embodiments will now be described, by way of non-limiting examples only, with reference to the accompanying drawings, in which:

[0044] Fig. 1 is a block diagram of a control system of the present disclosure for use in measuring one or more parameters of a three-dimensional patterned structure while protecting sensitive TEM image data;

[0045] Fig. 2 is a schematic illustration of how to protect sensitive information while providing “Data with positions” on parameters of interest;

[0046] Figs. 3A to 3D illustrate several embodiments of methods of the present disclosure, wherein Fig 3A exemplifies a “Manual Sanity” method; Fig 3B exemplifies automatic fit using TEM image processing software; Fig. 3C exemplifies an automatic fit using synthetic (simulated) TEM image generator; and Fig. 3D exemplifies an automatic fit using image processing of the synthetic TEM image; in all figures the region marked by the dashed line is operated by an IP owner.

[0047] DETAILED DESCRIPTION OF EMBODIMENTS

[0048] Reference is made to Fig. 1 showing, by way of a block diagram, a control system 10 of the present disclosure for use in metrology measurements of a patterned structure. The control system 10 is configured as or includes a computerized system including inter alia such functional parts as data input and output utilities 12 and 14, memory utility 16, communication utility 18, and data processing system 20. These parts of the control system 10 are typically formed by suitable hardware and / or software circuitries.

[0049] The control system 10 is configured and operable for data communication with a TEM data provider 40. The TEM data provider is associated with a system / entity performing high-resolution imaging of a region of interest of the patterned structure (i.e., TEM data owner system), and constitutes a storage device being either internal memory of such high-resolution imaging system or an external storage device connectable to said high-resolution imaging system. The control system may be associated with (connectable to or integral with) a metrology measurement system, or the functional parts of such control system can be distributed between a control system associated with the metrology measurement system and a control system associated with a TEM system of the TEM data provider.

[0050] Thus, as exemplified in Fig. 1, the control system 10 is associated with (i.e., is connectable to or is integral with) a dimensional metrology measurement system (not shown in the figure), and is connectable (e.g., via communication of any known suitable type) to the measured TEM data provider 40. This is exemplified in the figure by provision of suitable communication utilities 18 and 46 in the control system 10 and measured TEM data provider 40, respectively.

[0051] According to the technique of the present disclosure, input data received by the control system 10 from the TEM data provider, which is referred to herein as 1stTEM- related data, is TEM-related non-image data indicative of predefined position information associated with one or more measured geometric parameters of the region of interest of the patterned structure whose dimensional metrology measured data is to be interpreted. This TEM-related non-image data is extracted from measured TEM images by a controller / processor of the TEM imaging system (i.e., at the TEM image owner side), using predefined image processing recipe which is provided by the control system 10 associated with the metrology measurement system.

[0052] The data processing system 20 includes a dimensional metrology geometrical engine 22 configured and operable to provide a geometric model of a predetermined region of interest (ROI) of the patterned structure under examination.

[0053] As mentioned above, the dimensional metrology technique, typically being a spectroscopy-based technique, utilizes a model-based interpretation of metrology (optical) measured data to extract (geometrical) parameters of the patterned structure under examination. The geometric model is typically a 3D model of the patterned structure under examination, created by taking into account the physical constraints (from knowledge of semiconductor manufacturing process and trends), and includes parametrization, which allows to describe process variation in the manufacturing process.

[0054] It should be noted that techniques of 3D modeling of a patterned structure are known, and do not form part of the present invention. An example of such modeling technique is NOVAMARS® product commercially available from Nova Measuring Instruments. Such model is, for example, used for interpretation of Optical Critical Dimension (OCD) measurements.

[0055] The data processing system 20 at the metrology measurement side includes an image processing recipe provider 25 which utilizes the geometric model data to generate data indicative of the predefined image processing recipe. The latter is communicated to the TEM provider 40 side, and is used there, as described above, to generate the 1stTEM- related non-image data with respect to the corresponding ROI of the patterned structure.

[0056] The data processing system 20 further includes a synthetic (simulated) TEM image generator 26 configured and operable to utilize the geometrical model data received from the dimensional metrology geometrical engine 22 and generate image data indicative of a synthetic (simulated) TEM image data, referred to herein as 2ndTEM- related data, of the predetermined ROI of the patterned structure. As will be described more specifically further below, the 2ndTEM-related data may be simulated image data including a synthetic TEM image itself, or may be simulated non-image data including simulated position information associated with the one or more geometric parameters of the ROI of the patterned structure, extracted from the synthetic TEM image data using the predefined image processing recipe.

[0057] As described in the above-indicated patents US 10,916,404 and US 11450541, utilizing analysis of a match between synthetic TEM image data and measured TEM image data increases the robustness of structure parameters determination. This is mainly because the quality of measured TEM image data may be compromised due to low contrast appearance of some features (e.g., distances and angles between edges of 2D cross sections of the patterned structure). Also, the appearance of the edges-related and other features in a measured TEM image is strongly connected to the exact Lamellae position and thickness relative to the structure. Therefore, a comparison between measured TEM image data and synthetic (simulated) TEM image data may improve the accuracy of geometrical parameter(s) of the patterned structure and thereby assist in creation of an accurate geometrical model used to interpret measurements of the type different from TEM, e.g., interpret OCD spectral-based metrology measurements.

[0058] However, in cases when the owner of the measured TEM image data (e.g., the TEM tool vendor or an IC manufacturer) does not provide the raw TEM images of the patterned structure since such images may contain sensitive / confidential information / data, comparison between measured TEM data and synthetic TEM data cannot be utilized. The present disclosure provides the novel technique that allows a control system of the owner of the raw TEM images (at the measured TEM data provider 40 side) to automatically carry out certain analysis / processing of the raw TEM images using predefined image processing recipe received from the control system 10 associated with the metrology measurement system to provide (automatically or manually) to extract from the raw TEM images and provide to the control system 10 predefined position information associated with geometrical parameter(s) of the structure.

[0059] The measured TEM data provider 40 may thus include or be in communication with a TEM image processor 42. The latter includes a data extractor 48. The data extractor 48 is configured and operable to apply predetermined TEM image processing software (SW) to the raw TEM images of the patterned structure (received from the TEM tool / system) utilizing the predefined image processing recipe (received from the metrology measurement side, i.e. control system 10) to extract from the raw TEM images first TEM-related data being non-image data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure.

[0060] The term “TEM Image processing SW” in the present disclosure stands for a TEM images analysis SW, capable of assessing values of geometrical parameters from a TEM image automatically, without user interaction, based on a predefined TEM metrology recipe. Such SW may be owned by an IC manufacturer or by a third party (e.g., a TEM tool vendor), as long as it is approved by an IC manufacturer.

[0061] The term “Data” refers to a set of estimated parameter values for a TEM image or TEM Lamella.

[0062] The term “position information” / “Data with positions” refers to “Data” with the position where each data element can be found in a TEM image. Each position includes X and Y values in the image plane, in Angstroms (or nanometers, or meters, or other metric value of distance) relative to some image origin.

[0063] The position usually includes two points marking the start and the end of estimated value (e.g., of height or width determined by a TEM Image processing SW), but may include a single point, or several points, or may be an empty set in some cases.

[0064] These positions can be exported automatically by a TEM Image processing SW.

[0065] Examples of Data with positions characterized by two points may include: CD measurement at 50% including the position of the left CD border at 50% height and the position of the right CD border at 50% height; CD measurement at 10% including the position of the left CD border at 10% height and the position of the right CD border at 10% height; height measurement including the position of the feature edge at the bottom of the feature and position of the feature edge at the top of the feature.

[0066] An example of Data with position characterized by a single point may be an estimated curvature of an edge in a TEM image, where the position is the center of the arc of the circle.

[0067] An example of Data with position characterized by three points may be an estimated curvature of an edge in a TEM image, where the position includes the center of the measured arc, in addition to the locations of the beginning and end of the arc.

[0068] Using “Data with positions” approach of the present disclosure based on TEM- related non-image data obtained as described above (i.e., based on the metrology-side defined image processing recipe) allows the control system 10 related to the metrology side to effectively perform model-based measurements of the parameters of the structure without a need for TEM images themselves, and on the other hand, allows an IC manufacturer to avoid revealing sensitive TEM image information to an un-authorized person.

[0069] As mentioned above, the position information associated with one or more measured geometric parameters of the patterned structure and obtained / extracted from the raw TEM images using the predefined image processing recipe is referred to herein as “1stTEM-related data”. The data corresponding to / indicative of the synthetic TEM image data is referred to herein as "2ndTEM-related data" and may include image data (synthetic TEM image) or may be synthetic non-image data including position information associated with the one or more measured geometric parameters extracted from the synthetic EM images using the predefined image processing recipe.

[0070] As will be exemplified more specifically further below, if the data analysis technique of the present disclosure is implemented in its entirety at the metrology measurement side, then the control system 10 at the metrology measurement side generates and transmits to the TEM provider side the predefined image processing recipe and receives from the TEM provider side the 1stTEM-related non-image data. The control system generates the synthetic TEM image (2ndTEM-related image data) and analyzes the 1stand 2ndTEM-related data to determine and analyze correlation data indicative of a level of fit between them. If at least part of the data analysis is to be performed at the TEM provider side, then the control unit 10 transmits to the TEM data provider the 2ndTEM-related data (either the synthetic TEM image itself or simulated position information extracted from the synthetic TEM image using said predefined image processing recipe). At the TEM provider side, the 1stTEM-related data and the 2ndTEM- related data are analyzed to determine a level of fit between them and generate the correlation data which is transmitted to the control system at the metrology measurement side.

[0071] Thus, the data processing system 20 of the control system 10 at the metrology measurement side (or at the TEM provider side as the case may be) includes an optimization module 24 which is configured and operable to process input correlation data indicative of the level of fit between the 1stTEM-related data and the 2ndTEM- related data and generate an optimized geometric model for the predetermined ROI. The optimized geometric model can then be used for the model-based metrology measurements of the patterned structure, while, as described above, eliminating a need for measured TEM image data.

[0072] As shown in Fig. 1, in some embodiments, the control system 10 further includes an analyzer 28, configured and operable to analyze the 1stTEM-related data (received from the TEM data provider 40) and the 2ndTEM-related data (generated by the synthetic TEM image generator 26) and generate the correlation data which is then used by the optimization module 24 to provide the optimized geometrical model.

[0073] In some other embodiments, the TEM image processor 42 at the TEM data provider 40 side is configured and operable to receive and process the 2ndTEM-related data (provided by the control system 10 at the metrology measurement side). As noted above, the 2ndTEM-related data can include the synthetic (simulated) TEM image of the predetermined ROI of the patterned structure. In these embodiments, the extractor 48 may compare the raw measured TEM image (sensitive / confidential image data obtained at the TEM data provider side) to determine the level of fit between them and generate the correlation data which is then transmitted to the control system 10. Alternatively, the extractor 48 can apply to both the raw measured TEM image and the synthetic (simulated TEM image the TEM image processing software (SW) utilizing the predefined image processing recipe (received from the control system 10) to extract data indicative of position information associated with one or more measured geometric parameters of the patterned structure from each of these images. The TEM data provider can then provide the extracted position and simulated data to the control system 10 at the metrology measurement side for further analysis; or can analyze these data to determine the level of fit between them, and the same extractor 48 or another image processor at the TEM provider side as the case may be can then generate the corresponding correlation data and communicate the correlation data to the control system 10, i.e., to the optimization module 30. According to yet another examples of these embodiments, the 2ndTEM-related data supplied by the control system 10 at the metrology measurement side is in the form of synthetic position information extracted from the synthetic TEM image using the same predefined image processing recipe. The processor 48 at the TEM data provider side can thus analyze the synthetic position information over the 1stTEM-related non-image data to determine the level of fit between them and generate the correlation data, which is communicated to the control system 10 at the metrology measurement side.

[0074] Thus, in some embodiments the correlation data indicative of the level of fit between the 1stTEM-related data and the 2ndTEM-related data is calculated / provided by the measured TEM data provider 40, while in other embodiments, the correlation data is calculated / provided by the dimensional metrology owner, as will be described in detail below.

[0075] The optimization module 24 utilizes the correlation data and provides an accurate / optimized geometrical model thereby allowing to perform accurate metrology of the patterned structure, while protecting sensitive intellectual property found in measured TEM images.

[0076] In the embodiments where the 2ndTEM-related data includes the synthetic (simulated) TEM image of the predetermined ROI of the patterned structure, the analyzer 28 (or image processor at the TEM data provider as the case may be) may perform matching of the 1stTEM-related data to relevant edges of the synthetic (simulated) TEM image and calculate a corresponding correlation data / score, as will be described below.

[0077] Reference is made to Fig. 2 which shows an example of the technique of the present disclosure to provide additional geometrical parameters from the analysis of the raw TEM images (without compromising sensitive IP of measured TEM data provider / IC manufacturer). Fig. 2 shows a schematic representation of a TEM image 60 showing cross sections of element / feature A, including the parameters of interest (POI), and element / feature B representing confidential data. The analysis of element A includes 3 critical dimension (CD) estimates, CD1, CD2, and CD3 at 20%, 50% and 80% of the height, respectively. The position of each CD includes two points (coordinates of the left and right edges of the CDs indicated by arrows).

[0078] Typically, the owner of raw TEM image would provide the dimensional metrology owner with only a single CD, e.g., the two points / coordinates corresponding to CD2. This data may be insufficient for the dimensional metrology analyzer to calculate an accurate dimensional metrology model of the structure being inspected.

[0079] Therefore, according to the technique of the present disclosure, and in the nonlimiting example of Fig. 2, the owner of the raw TEM image may be instructed to provide the 1stTEM-related non-image data including a total of 6 points, corresponding to the edges of three CDs, e.g., CD1, CD2 and CD3 of Fig. 2, while the raw TEM image itself, including the confidential structure B, is not revealed. The right side of Fig. 2 represents this “Data with positions”, being an exemplary 2ndTEM-related non-image data, corresponding to the 6 points (defined by the respective ends of the double-sided arrows) designating the edges of element A at the various heights.

[0080] Thus, providing “Data with positions” instead of raw TEM images allows an IC manufacturer to avoid revealing sensitive TEM image information (e.g., the element B in image 60) to an un-authorized person. As will be described in more detail below, this measured (geometrical) data, i.e., the data with positions, e.g., data corresponding to CD1, CD2 and CD3 data indicative of predefined image processing recipe in the example of Fig. 2, can be used by the analyzer of the control system 10 at the dimensional metrology measurement side to create an optimized accurate dimensional metrology model that serves to extract the corresponding geometrical parameters during the interpretation of dimensional metrology measurements.

[0081] The provided geometrical parameters may include one or more of the following (but are not limited to): structure widths (CD) at 10%, 30%, 50%, 70% and 90% height, estimated from a TEM image; widths (CD) of multiple devices (transistor, periodic parts of a microchip) across TEM lamella; heights of different features of interest, estimated from a TEM image; heights of multiple devices (transistors, periodic parts of microchip) across TEM lamella; Parameters of Interest (POI) such as Rounding, Undercut, Overetch, Under-etch, Gap distance, Minimal Shortage distance, Diagonal distance between different parts of microchip, estimated from a TEM image; and Multiple estimates of the above POIs (Rounding, Undercut, Over-etch, Under-etch, Gap distance, Minimal Shortage distance, Diagonal distance) across TEM lamella.

[0082] Reference is made to Fig. 3A exemplifying, by way of a flow diagram 100, a method of the present disclosure termed “Manual Sanity”. In the figure, the blocks enclosed in a dashed-line rectangle correspond to the steps executed at the TEM data provider side (associated with the owner of raw TEM images).

[0083] In step 110, an initial geometrical model 120 of a patterned structure (or ROI of the patterned structure) is provided by the dimensional metrology geometrical engine 22 based on a predetermined number of CDs and parameters of interest (POIs).

[0084] It should be noted that generally, the dimensional metrology owner holds an initial information, at least, regarding the functionality of the components of the patterned structure under consideration. This initial information enables the dimensional metrology owner to create an initial geometrical model 120 that may be used for further optimization based on more accurate TEM measurements, as described below.

[0085] The geometrical model 120 serves as the basis for generation of the 2ndTEM- related data which, in this non-limiting embodiment, is a synthetic (simulated) TEM image 130 (TEMsim). Also, the geometrical model serves for generation of predefined image processing recipe (step 122) which is used at the TEM data provider side to extract, from the measured TEM image data, the 1stTEM-related non-image data.

[0086] A measured TEM image or a TEM lamellae image 140 (TEMmeas ) obtained by measured TEM data provider 40 is thus processed (step 150) according to the recipe provided by the metrology measurement side, while under the control of the TEM image owner side. The TEM image processing results in generation of the 1stTEM-related data 160 being non-image measured data indicative of predefined position information (Data with positions) associated with one or more measured geometric parameters of the ROI of the patterned structure.

[0087] For example, referring back to Fig. 2 above, the “Data with positions” provides the dimensional metrology owner with more detailed information than only the length of a single arrow (e.g., CD2) representing the needed critical dimension. The 1stTEM- related data 160 includes more detailed information related to geometrical parameters of the ROI of the patterned structure, e.g., coordinates (x,y) of both left and right hand of each arrow (instead on length only) corresponding to CD1, CD2 and CD3. This 1stTEM- related data 160 is provided to the dimensional metrology owner without compromising the confidentiality of the IC manufacturer. In this example, the steps 140 to 160, enclosed by a dashed rectangle, are operated by the IP owner.

[0088] Then, in step 170, the dimensional metrology owner overlays the received 1stTEM-related non-image data (Data with positions) 160 on top of the synthetic TEM image 130 to (visually) analyze the fit between them and (manually) adjust parameters of the synthetic TEM image 130 (TEMsimor 2ndTEM-related data) such as alignment and material contrast, geometrical model parameters, geometrical engine definitions, etc.

[0089] In this example, the comparison is performed by the dimensional metrology user based on visual match and may be considered as a “sanity check” procedure. This step of comparison and adjustment may involve repeating steps 110 to 130, such that an optimized accurate dimensional metrology model 180 is obtained as a final result.

[0090] The “Manual Sanity” method of Fig. 3A may serve as the first evaluation aimed to clarify whether the chosen geometrical model 120 of the patterned structure is acceptable or not.

[0091] It should be noted that the 1stTEM-related data (Data with positions) in the method of Fig. 3A is extracted by the TEM image processing SW of the owner of the raw TEM image data. Typically, the dimensional metrology owner uses a different processing SW to extract the corresponding POIs from the synthetic (simulated) TEM images. The inventors sought to reduce the potential amount of mismatch / error during the process of image analysis (of, respectively, raw measured TEM data and synthetic / simulated TEM data).

[0092] Reference is made to Fig. 3B exemplifying by way of a flow diagram 200 a method of an automatic fit using (uniform) TEM image processing SW.

[0093] As in Fig. 3A, a measured TEM image or a TEM lamellae image 140 (TEMmeas ) is provided at the TEM image owner side (at the TEM data provider). Then, in step 210, at the TEM data provider side, the measured raw TEM image undergoes an automatic TEM image processing using predefined software supplied / defined by the dimensional metrology owner. This software includes a predefined image processing recipe aimed at generating from the raw measured TEM images the 1stTEM-related non-image data (measured data with positions data) 240, i.e., the required geometric parameters in a format that does not compromise sensitive image of the structure's elements.

[0094] As in step 110 of Fig. 3A, the dimensional metrology geometrical engine 22 calculates an initial geometrical model 120 of the patterned structure based on a predetermined number of CDs and parameters of interest (POIs). Similar to step 130 of Fig. 3A, the control system at the dimensional metrology owner side simulates a synthetic TEM image (2ndTEM-related data) based on the geometric model of the patterned structure.

[0095] Then, in step 220, the simulated TEM image is provided to the TEM data provider side (owner of the measured raw TEM image) where the simulated TEM image undergoes analysis using the same automatic TEM image processing SW (based on predefined recipe) as the one used at the TEM data provider side to analyze the real measured TEM image. The result of this analysis is the generation of 2ndTEM-related data (Data with positions) 230 including the same predetermined number of CDs and parameters of interest (POIs), however extracted from the simulated TEM image in step 210. The steps 210 and 220 are performed at the TEM data provider side, eliminating thus the need to reveal confidential / sensitive IP (i.e., raw data TEM images). On the other hand, the use of the same automatic TEM image processing (based on predefined recipe) provides the confidence that the correct data indicative of the CDs / POIs is obtained from both the raw TEM images and the simulated TEM images. Then, in step 250, automatic calculation of correlation data, e.g., a merit function, is performed (e.g., by the analyzer 28), indicative of matching between measured (240) and simulated (230) data (for example, sum of squared differences). For example, the same automatic TEM image processing SW calculates the edge points (the 6 coordinates of the 3 arrows representing CD1, CD2 and CD3) of the element of interest A in Fig. 2, in the simulated and measured TEM images and the results are compared.

[0096] The correlation data is used (e.g., by the optimizing module 24), if necessary, to optimize / update the geometrical model 120 of the simulated TEM image. If necessary, a new simulated TEM image is generated (repeated step 130). The updated 2ndTEM image data (simulated Data with positions) 230 is then again compared with the 1stTEM -related non-image data (measured Data with positions) 240 and a new merit function is calculated (step 250). Thus, an optimized and accurate geometrical model 180 may be generated, that is used during the analysis of the measured metrology data by the dimensional metrology owner.

[0097] Reference is made to Fig. 3C exemplifying by way of a flow diagram 300 a method of automatic fit using synthetic (simulated) TEM image generation. The method of Fig. 3C is generally similar to that described with reference to Fig. 3B, except for the data used at the dimensional metrology measurement side to perform optimization of the geometrical model.

[0098] In the embodiment described in Fig. 3C no geometrical data (Data with positions) is received at the control system of the dimensional metrology measurement side as in Fig. 3B. In this method, the TEM provider side (associates with the owner of the raw measured TEM images) receives the simulated TEM image (TEMsim), in step 310 and automatically calculates / extracts the 1stTEM-related data (measured Data with positions) 340 and 2ndTEM-related data (simulated Data with positions) 330 from, respectively, the raw measured TEM image or a TEM lamellae image 140 (TEMmeas ), and the simulated TEM image (TEMsim).

[0099] Then, in step 350, an automatic calculation of a correlation data (e.g., merit function) is performed at the TEM data provider side, e.g., by the extractor 48, for matching between measured (340) and simulated (330) data (for example, sum of squared differences). The resulting merit function is provided to the control system of the dimensional metrology measurement side where the geometrical model 120 from which the simulated TEM image is generated can be updated is necessary, and a new simulated TEM image is generated (repeated step 130). The updated simulated TEM image (TEMsim) may then be provided again to the TEN data provider side where a new merit function can be calculated based on newly extracted 2ndTEM-related data (simulated Data with positions) 330. Thus, an optimized and accurate geometrical model 180 is generated, for use during the analysis of the measured metrology data by the dimensional metrology owner, without compromising the confidentiality of IP information of the IC manufacturer.

[0100] It should be noted that, as in the example of Fig. 3B, image processing performed at the TEM data provider side uses automatic TEM image processing SW which is based on the predefined recipe conforming to the requirements of the dimensional metrology measurement side to provide the required geometrical parameters data for calculating an optimized and accurate dimensional metrology model 180.

[0101] Reference is made to Fig. 3D exemplifying by way of a flow diagram 400 a method of automatic fit according to the principles of the present disclosure. In this method, the steps 110 to 130 and 140 to 160 are similar to those described with reference to Fig. 3A. The difference with respect to the example of Fig. 3A is that in this method no simulated Data with positions is used. The input 1stTEM-related non-image data (measured Data with positions) 160 is received at the control system of the metrology measurement side from the TEM data provider and the 2ndTEM-related data including the synthetic (simulated) TEM image is generated as described above. Then, in step 410, correlation data is determined including an automatic score (i.e., merit function) for matching the 1stTEM-related non-image data (measured / extracted Data with positions) 160 to relevant edges of the synthetic (simulated) TEM image.

[0102] For example, referring again to Fig 2, the synthetic (simulated) TEM image may include the cross section of element A only (element B representing confidential data and therefore is not included in the simulated image). As described above, the raw TEM image data owner (TEM data provider) may be instructed to provide the dimensional metrology measurement side with the 1stTEM-related non-image data (measured Data with positions) including a total of 6 points, corresponding to the positions (coordinates) of the edges of three CDs, e.g., CD1, CD2 and CD3 of Fig. 2. The automatic score is calculated indicating how well the received positions CD1, CD2 and CD3 (on the right side of Fig. 2) match the edges of element A on the synthetic (simulated) image.

[0103] The calculation of the automatic score is based on predefined recipe and area of interest definitions from the dimensional metrology engine 22. As in all previous embodiments described above, the method of flow diagram 400 may include iterative steps of repeated calculation of the geometrical model 120 and generation of the synthetic(simulated) TEM image (TEMsim) to provide an optimized and accurate dimensional metrology model 180.

[0104] Thus, the present disclosure provides a novel technique for use in metrology measurements enabling to maintain confidentiality in integrated circuits manufacturing metrology, which is essential to protect innovation, security and market competitiveness. The strategy of selective disclosure used in the technique of the present disclosure helps to ensure that metrology vendors receive only the necessary information without compromising sensitive IP.

Claims

CLAIMS:

1. A control system for use in metrology measurements of a patterned structure, the control system being configured and operable for data communication with a High Resolution Imaging (HRI) data provider and comprising a computerized system comprising data input and output utilities, memory, and a data processing system, wherein the data processing system comprises: a dimensional metrology geometrical engine configured and operable to provide a geometric model of a predetermined region of interest (ROI) of the patterned structure; an image processing recipe provider configured and operable to generate data indicative of predefined image processing recipe; a synthetic (simulated) image data generator configured and operable to utilize said geometrical model of the predetermined ROI and generate image data corresponding to a synthetic HRI image data of said predetermined ROI; an optimization module configured and operable to carry out the following: process input correlation data indicative of a level of fit between first HRI-related data being a non-image measured data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure, and second HRI-related data comprising data indicative of said synthetic HRI image data, and generate an optimized geometric model of said predetermined ROI, thereby allowing use of the optimized geometric model for the metrology measurements of the patterned structure, while eliminating a need for measured HRI image data of the ROI for optimization of the geometric model used in the metrology measurements.

2. The control system according to claim 1, wherein said HRI image data comprises any one of the following data providing detailed insights into features of the patterned structure: Transmission Electron Microscopy (TEM) data, Scanning Transmission Electron Microscopy (STEM) data, Energy Dispersive X-ray Spectroscopy (EDX) data, Electron Energy Loss Spectroscopy (EELS).

3. The control system according to claim 1, being configured and operable to carry out the following: transmit, to the HRI data provider, data indicative of the predefined image processing recipe thereby enabling the HRI data provider to generate said first nonimage data using predefined image processing recipe.

4. The control system according to claim 3, configured and operable to transmit data indicative of the synthetic HRI image data to the HRI data provider and receive said correlation data from the HRI data provider.

5. The control system according to claim 3, being configured and operable to receive said first non-image HRI-related data from the HRI data provider, the control system further comprising an analyzer configured and operable to receive and analyze said first and second HRI-related data and generate the correlation data.

6. The control system of claim 1, said second HRI-related data comprises a synthetic HRI image.

7. The control system of claim 1, wherein said second HRI-related data comprises simulated position information associated with said one or more geometric parameters of the ROI of the patterned structure, extracted from the synthetic HRI image data using said predefined image processing recipe.

8. The control system of claim 1, wherein said correlation data comprises a matching score for overlay of each of said one or more measured geometric parameters on top of said synthetic HRI image.

9. The control system of claim 1, wherein said correlation data comprises matching scores indicative of a match between the predefined position information and corresponding edges identified in the synthetic HRI image data.

10. The control system of claim 1, wherein said correlation data comprises an automatically calculated merit function describing the level of fit between the first and second HRI-related data.

11. The control system of claim 1, wherein the predefined position information associated with one or more measured geometric parameters of the ROI comprises X and Y coordinates in a measured HRI image plane relative to an image origin.

12. The control system of claim 1, wherein the predefined position information for at least one of said one or more geometric parameters comprises start and end points for a measured feature.

13. The control system of claim 1, wherein the position information for at least one of said one or more geometric parameters comprises a single point.

14. The control system of claim 1, wherein the position information for at least one of said one or more geometric parameters comprises three or more points.

15. The control system of claim 1, wherein the one or more geometric parameters comprise one or more of the following: widths' values at corresponding parts of a total height of a feature, heights of features, parameters of interest including rounding, undercut, over-etch, under-etch, gap distance, or diagonal distances.

16. The control system of claim 8, wherein the optimization comprises adjustment of one or more parameters of the geometric model, based on the correlation data indicative of visual comparison of the predefined position information overlaid on the synthetic HRI image data.

17. A control system for use in metrology measurements of a patterned structure, the control system comprising a computerized system comprising data input and output utilities, memory, and a data processing system, wherein the data processing system comprises: a dimensional metrology geometrical engine configured and operable to provide a geometric model of a predetermined region of interest (ROI) of the patterned structure; a synthetic (simulated) image data generator configured and operable to utilize said geometrical model of the predetermined ROI and generate image data corresponding to a synthetic High Resolution Image (HRI) data of said predetermined ROI; an analyzer configured and operable to receive and analyze first and second HRI- related data and generate correlation data indicative of a level of fit between said first and second HRI-related data, wherein: the first HRI-related data is a non-image measured data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure, and the second TEM-related data comprises data indicative of said synthetic TEM image data,; an optimization module configured and operable to utilize said correlation data to perform optimization of the geometric model of said predetermined ROI and generate an optimized geometric model , thereby allowing use of the optimized geometric model for the metrology measurements of the patterned structure, while eliminating a need for measured HRI image data of the ROI for optimization of the geometric model used in the metrology measurements.

18. The control system according to claim 17, wherein said HRI data comprises any one of the following data providing detailed insights into features of the patterned structure: Transmission Electron Microscopy (TEM) data, Scanning Transmission Electron Microscopy (STEM) data, Energy Dispersive X-ray Spectroscopy (EDX) data, Electron Energy Loss Spectroscopy (EELS).

19. The control system of claim 17, wherein said first HRLrelated data, being the nonimage measured data, is extracted from an HRI measured image of said ROI using a predefined image processing recipe.

20. The control system of claim 19, said second HRLrelated data comprises a synthetic HRI image.

21. The control system of claim 19, said second HRLrelated data comprises simulated position information associated with said one or more geometric parameters of the ROI of the patterned structure, extracted from the synthetic HRI data using said predefined image processing recipe.

22. The control system of claim 19, wherein said correlation data comprises a matching score for overlay of each of said one or more measured geometric parameters on top of said synthetic HRI image.

23. The control system of claim 19, wherein said correlation data comprises matching scores indicative of a match between the predefined position information and corresponding edges identified in the synthetic HRI image data.

24. The control system of claim 20, wherein said correlation data comprises an automatically calculated merit function describing the level of fit between the first and second HRLrelated data.

25. The control system of claim 18, wherein the predefined position information associated with one or more measured geometric parameters of the ROI comprises X and Y coordinates in a measured HRI image plane relative to an image origin.

26. The control system of claim 18, wherein the predefined position information for at least one of said one or more geometric parameters comprises start and end points for a measured feature.

27. The control system of claim 18, wherein the position information for at least one of said one or more geometric parameters comprises a single point.

28. The control system of claim 18, wherein the position information for at least one of said one or more geometric parameters comprises three or more points.

29. The control system of claim 18, wherein the one or more geometric parameters comprise one or more of the following: widths' values at corresponding parts of a total height of a feature, heights of features, parameters of interest including rounding, undercut, over-etch, under-etch, gap distance, or diagonal distances.

30. The control system of claim 22, wherein the optimization comprises adjustment of one or more parameters of the geometric model, based on the correlation data indicative of visual comparison of the predefined position information overlaid on the synthetic TEM image data.

31. A method for use in metrology measurements of a patterned structure, the method comprising: providing a geometric model of a predetermined region of interest (RO I) of the patterned structure; providing data indicative of predefined image processing recipe; utilizing said geometrical model of the predetermined ROI and providing simulated image data corresponding to a synthetic High Resolution Imaging (HRI) image data of said predetermined ROI; processing correlation data indicative of a level of fit between first HRI-related data being a non-image measured data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure, and second HRI-related data comprising data indicative of said synthetic HRI image data, and generating an optimized geometric model of said predetermined ROI, thereby allowing use of the optimized geometric model for the metrology measurements of the patterned structure, while eliminating a need for measured HRI image data of the ROI for optimization of the geometric model used in the metrology measurements.

32. The method according to claim 31, comprising communicating with an HRI data provider to transmit to the HRI data provider data indicative of the predefined imageprocessing recipe thereby enabling the HRI data provider to generate said first non-image data using predefined image processing recipe.

33. The method according to claim 32, comprising transmitting data indicative of the synthetic HRI image data to the HRI data provider, and receiving said correlation data from the HRI data provider.

34. The method according to claim 32, comprising receiving said first non-image HRI-related data from the HRI data provider further and analyzing said first and second HRI-related data and generating the correlation data .

35. The method according to claim 31, wherein said HRI data comprises any one of the following data providing detailed insights into features of the patterned structure: Transmission Electron Microscopy (TEM) data, Scanning Transmission Electron Microscopy (STEM) data, Energy Dispersive X-ray Spectroscopy (EDX) data, Electron Energy Loss Spectroscopy (EELS).

36. A method for use in metrology measurements of a patterned structure, the method comprising: providing a geometric model of a predetermined region of interest (RO I) of the patterned structure; providing data indicative of predefined image processing recipe; utilizing said geometrical model of the predetermined ROI and providing simulated image data corresponding to a synthetic High Resolution Imaging (HRI) image data of said predetermined ROI; analyzing first HRI-related data being a non-image measured data indicative of predefined position information associated with one or more measured geometric parameters of the ROI of the patterned structure, and second HRI-related data comprising data indicative of said synthetic HRI image data, and generating correlation data indicative of a level of fit between the first and second HRI-related data; processing the correlation data and generating an optimized geometric model of said predetermined ROI, thereby allowing use of the optimized geometric model for the metrology measurements of the patterned structure, while eliminating a need for measured HRI image data of the ROI for optimization of the geometric model used in the metrology measurements.

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